Active-contour-using-Matlab

bi-linear Filter. Circular Convolution of two sequences using DFT and IDFT. Discrete Fourier Transform (DFT) of the sequence

application of fuzzy logic in fluid

localized segmentation is implemented

本程序针对一类不确定性非线性系统，利用输出反馈实现系统镇定，我用simulink做的一个仿真例子。(This procedure for a class of uncertain nonlinear systems using output feedback stabilization realize system, I used to do a simulink simulation example.)

毕业设计论文中编写的关于生成一个雷克子波(Graduate design thesis writing Leike Zi generate a wave)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )

Speech Enhancement A Bayesian Estimation Approach Using Gaussian Mixture Model

T型三电平控制PMSM仿真，用于永磁同步电机控制，逆变器 三相逆变器 适合 在matlab环境下运行(PMSM simulation of T-type three-level control for permanent magnet synchronous motor control. Inverter three-phase inverters are suitable for operation in MATLAB environment.)

In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system). Instead of dealing with only one possible reality of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths may be more probable and others less.

说明：  采用matlab编写，基于OPTA细化算法，适合入门级新手用，希望对大家有用(Using matlab to prepare, based on OPTA thinning algorithm, suitable for entry-level novice, and I hope useful for all of us)